Currently, there is a need in the lubrication industry for an improved method that would allow prediction of base oil properties and more specifically, allow the formulation of base stock and lubricants with specific properties. At present, there is no method available that would allow the prediction of base oil properties. Base oils are presently characterized by ASTM, API, and DIN methods. These methods are time consuming and require large amount of sample quantity. There have been efforts to predict base oil properties from structural information obtained from NMR spectroscopy. For example, Shea et al. (1) use the NMR spectroscopy and Neural network for this purpose. B. K. Sharma et al. (2) correlated pressure viscosity coefficient of Lubricant Base oils with structural parameters obtained from NMR Spectroscopy. Gatto et al. (3) correlated the physical properties and antioxidant response of hydrocracked base oils and polyalphaolefins to chemical composition determined by mass spectrometry. However, most of these studies are limited to the specific set of samples or specific analytical technique. There has been no reported knowledge using various analytical techniques, in addition to NMR, to improve prediction capability of the Base Oil Model. More importantly, there is no method available that would allow the formulation of base oil and lubricants with specific properties.
The Base Oil Predictor preferably has a user-friendly interface. One could select from the menu up to, e.g., four base oils from, e.g., about 40 base oils available at the U.S. blending units. With single click it would be possible to predict base oil properties of the blends that includes VI, Vis40, Vis100, OxBN, Pour Point, Cloud Point and NOACK Volatility. In a preferred embodiment, it incorporates more base oils.
Testing is done by: testing the model for the blends with known properties, preparing a blend of two, three and four base oils in known composition and submit the blend for the above test as well as for the NMR analysis.
Various studies (1-3) reported in the literature recognize that most of the bulk properties of base oils result from hydrocarbon type distribution in the base oil. During last 60 years, NMR Spectroscopy has become one of the major analytical tools for the structural determination of hydrocarbons. Other techniques used for the characterization of the hydrocarbons are MS and HPLC. In the present invention, structural parameters of the base oil are determined by these analytical techniques such as NMR, HPLC-UV and FIMS. Base oils are further characterized using SIMDIST and VPO to get the boiling point distribution and molecular weight respectively. These structural parameters are then modeled against the experimentally observed physical properties of the initial set of base oils. An artificial neural network is used to develop such a model. A number of properties that could be modeled include, but not limited to, Kinematic Viscosity at 40° C., Kinematic Viscosity at 100° C., Viscosity Index, Pour Point, Cloud Point, Oxidation Performance, etc.
As part of the initial study, we selected 20 base oil samples from Group I, II and III. For the characterization by NMR spectroscopy, each base oil is characterized using a quantitative 13C NMR technique. Once a spectrum is acquired, integrals are recorded over several regions to differentiate various types of carbons. All these samples were further characterized using analytical techniques mentioned above and the structural parameters determined using these techniques is presented in table 1. These structural parameters are modeled using NeuroShell predictor Release 2.2 software from Ward System Group. All models were constructed using a back-propagation algorithm. A separate and distinct model is constructed for each property. After a model is developed a correlation coefficient is obtained. A comparison of the expected values and estimated values for the Viscosity Index, Kinematic Viscosity at 40° C., Kinematic Viscosity at 100° C., Pour Point, and Oxidation Performance is presented in Tables 2, 3, 4 and 5 respectively. The correlation coefficient obtained for various models suggest that the structural features derived from various analytical techniques accurately model various physical properties of the base oil. This technique has a value in 1) predicting base oil blends that will match desired performance, 2) streamline in handling base oil changes, 3) identify synthetic target molecules to improve performance, 4) reduce the amount of laboratory testing and 5) design new catalyst to maximize key attributes. More work is underway to improve the model further and to include base oils with more diverse properties.